[1] Lan Zhuorui, Xia Weiwei, Wu Siyun, Yan Feng, et al. Joint wireless and cloud resource allocationbased on parallel auction for mobile edge computing [J]. Journal of Southeast University (English Edition), 2019, 35 (2): 153-159. [doi:10.3969/j.issn.1003-7985.2019.02.002]
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Joint wireless and cloud resource allocationbased on parallel auction for mobile edge computing(
)
移动边缘计算系统中基于并行拍卖的无线资源 与云资源联合分配
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 35
- Issue:
- 2019 2
- Page:
- 153-159
- Research Field:
- Information and Communication Engineering
- Publishing date:
- 2019-06-30
Info
- Title:
- Joint wireless and cloud resource allocationbased on parallel auction for mobile edge computing
- 移动边缘计算系统中基于并行拍卖的无线资源 与云资源联合分配
- Author(s):
- Lan Zhuorui, Xia Weiwei, Wu Siyun, Yan Feng, Shen Lianfeng
- National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
- PACS:
- TN929.5
- DOI:
- 10.3969/j.issn.1003-7985.2019.02.002
- Abstract:
- A joint resource allocation algorithm based on parallel auction(JRAPA)is proposed for mobile edge computing(MEC). In JRAPA, the joint allocation of wireless and cloud resources is modeled as an auction process, aiming at maximizing the utilities of service providers(SPs)and satisfying the delay requirements of mobile terminals(MTs). The auction process consists of the bidding submission, winner determination and pricing stages. At the bidding submission stage, the MTs take available resources from SPs and distance factors into account to decide the bidding priority, thereby reducing the processing delay and improving the successful trades rate. A resource constrained utility ranking(RCUR)algorithm is put forward at the winner determination stage to determine the winners and losers so as to maximize the utilities of SPs. At the pricing stage, the sealed second-price rule is adopted to ensure the independence between the price paid by the buyer and its own bid. The simulation results show that the proposed JRAPA algorithm outperforms other existing algorithms in terms of the convergence rate and the number of successful trades rate. Moreover, it can not only achieve a larger average utility of SPs but also significantly reduce the average delay of MTs.
- 提出了一种移动边缘计算场景下基于并行拍卖的无线资源与云资源联合优化分配算法.该算法将无线资源与云资源的联合分配建模为拍卖过程, 旨在最大化资源供应者的效用, 同时满足用户时延需求.该拍卖包括投标、胜者决定以及定价阶段.在投标阶段, 用户综合考虑可用资源以及距离等因素来决定投标向量和投标优先级, 从而减少处理时延, 提高成功交易率.在胜者决定阶段, 提出基于资源约束的效益排序算法来决定拍卖的胜者与失败者, 从而最大化资源供应者的效益.在定价阶段, 采用密封次高价定价法来保证资源定价与投标价格的独立性.仿真结果表明, 与现有算法相比, 所提算法收敛速度更快, 成功交易率更高, 资源提供者的平均效益和用户任务处理时延更优.
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Memo
- Memo:
-
Biographies: Lan Zhuorui(1994—), female, graduate; Xia Weiwei(corresponding author), female, doctor, associate professor, wwxia@seu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No.61741102, 61471164, 61601122).
Citation: Lan Zhuorui, Xia Weiwei, Wu Siyun, et al.Joint wireless and cloud resource allocation based on parallel auction for mobile edge computing[J].Journal of Southeast University(English Edition), 2019, 35(2):153-159.DOI:10.3969/j.issn.1003-7985.2019.02.002.